Ajay SableandClaude Opus 4.8
678e0764a5
New "Studio" tab (/concept): drop a garment shape + a fabric swatch, describe the item, and generate a concept preview. Proxies SAPL's commissioned AIBrandSolutions Swatch→Garment backend server-side (no vendor creds needed), persists the result to data/concepts/, and serves it same-origin behind our login/RBAC. - backend/routers/concept.py: POST /api/concept/swatch-to-garment (moodboard:edit) - config: swatch_to_garment_base - main.py: mount /concepts (fresh-checkout-safe mkdir) + register router - frontend: ConceptStudio page, "Studio" nav tab, swatchToGarment()/conceptImageUrl() - docs: 24-Jun design-library meeting notes + STATUS entry Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
6.5 KiB
Created on June 24, 2026 by Summary AI
Summary of Design Library & Workflow
Action Items
Speaker A Create Zara Men ’ s 25 ‑ shirt collection
Meet with Zara Men's department to develop a collection of 25 shirts, prepare mood board(s) focused on embroidered styles, and produce a buyer-ready presentation (PDF/AI) from the selected items.
Speaker A Resource 10 fabric/sample items
Inform the fabric team to resource the 10 specific fabric/sample items required by the mood board brief, identify which samples are missing, and flag availability in the library for the upcoming collection.
Overview
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Goal: create an integrated design ecosystem that combines mood boards, a searchable library, fabric entries and garment collections to speed buyer presentations and sampling.
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Proposed pipeline: decode mood boards → search repository → surface matches (top ~50) →
refine selection → produce curated presentation .
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Key capacity targets mentioned: a sample Zara Men's collection of 25 shirts , presenting 50 –
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candidate items and refining to 10 20 for closure; fabric resourcing of 10 items.
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System features prioritized: mood-board decoder , fabric vs garment pages, QR-based sample tracking, a smart feed/homepage and strong search/filter logic.
Vision & high-level goal
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The ambition is to redefine how design happens in the company by moving from manual mood-board handling to an ecosystem that supports discovery, selection and handoff to production.
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Outcome expected: faster buyer presentations, clearer briefs for design and production, and traceability of samples and orders.
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The system should sit on everyone ’ s desktop (intranet-style) and be live, accessible globally via login.
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Next: decide whether to stay at strategy level or start building the mood-board decoding MVP immediately.
Three content buckets (catalog model)
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Bucket 1 — Brand research / what exists: curated bank of leading brands scraped continuously (A → Z) to capture new products, collections and trends; accessible to everyone.
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Bucket 2 — Our collections: repository of in-house produced concepts, developments and finished samples (production assets).
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Bucket 3 — Interactive / ideation: live actions for "what to create today" — mood-board ingestion, decode and immediate concept generation that connects back to Buckets 1 & 2.
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The interactive bucket links the other two: decode a mood board, pull matches from research and internal library, then propose new concepts or samples.
Mood-board decoding workflow
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User uploads a mood board (image, voice or text directions). The decoder should extract colors, prints, fabrics, shapes and value ‑ add elements and present these as clear, infographical sections.
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Decoder outputs a ranked list of relevant garments/fabrics (example: show 50 matching items) and –
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allows the user to select a refined set (e.g., 10 20 ).
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The UI should show the original mood board next to extracted palettes/tags and provide filters for quick narrowing.
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Next: implement a live example run to validate extraction accuracy and resource usage (noted current runs take ~5 minutes and are resource ‑ intensive).
Library, boards and sharing model
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The library is a normalized repository where a single canonical entry can live on multiple boards (same entry usable in 100 boards).
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Boards are collections (e.g., buyer Sveira) and can be published/finalized; published boards become shareable like Pinterest with tags, descriptions and comments.
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A homepage/feed should surface recent or relevant board updates (smart feed, not a dumb chronological stream).
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Search bar and filter logic must be strong: support layman queries ("black blouse", "resort collar", "floral polyester") and role-based views (Aaron vs intern) via filters/permissions.
— Fabrics vs garments UI and linking
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Recommendation: create a separate page for fabrics (distinct from garment feed) and enable linking between fabric entries and garments.
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Allow superimposition: users can overlay any fabric onto any garment shape or model and request edits (lengthen, add pockets, frill, lace).
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Fabric entries should include sample availability and be linked to inventory/samples when present; otherwise show as concept.
Sample tracking & QR functionality
Each physical sample can be uploaded and assigned a QR / item number . Scanning the QR opens the sample's page showing repository entry, availability and location.
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Bulk scanning should update sample movements (e.g., moving 25 samples to Delhi) so the system always knows sample whereabouts and repository status.
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Fabric entries should also support sample-level records so linked garments can reference the actual sample used.
Feed, discovery & scraping
Implement continuous scraping of leading brands to populate the research bucket; provide curated – brand catalogs (start with 10 30 reference brands, expandable).
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The feed/homepage will surface important design events: newly uploaded boards, new fabric collections, trending brand finds and urgent orders.
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The feed must be "smart": prioritize recent, relevant and high impact items rather than flooding users with everything.
Order / production tracking (high level)
When orders arrive (email or system), create a single source of truth: parse order email, extract buyer, date, quantities and price.
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System should create a lot number, generate a TNA/plan and prompt 3 4 key questions (cost sheet ‑
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upload, fabric lead time, value adds, production time).
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If stakeholders do not reply within 24 hours , escalate with alerts and mark the order as pending/undecided.
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Visual order dashboard: one-page view of all orders, with red/blinking flags for issues and milestone tracking for trims, cutting, stitching and finishing.
Priorities & recommended next steps (strategic)
Short term (build/validate): implement mood-board decoder MVP , library indexing/search and separate fabric page to validate core discovery flows.
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Medium term (productize): add QR-based sample tracking, role-based filters/views, feed/homepage and board publishing/sharing.
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Operational steps: standardize what to upload (samples, concepts, boards), define minimal metadata (colors, fabrics, shapes, buyer/context) and onboard the team to start uploading content immediately.